Optimization of Bathymetry Estimates for Nearshore Hydrodynamic Models Using Bayesian Methods
Publication: Journal of Waterway, Port, Coastal, and Ocean Engineering
Volume 144, Issue 6
Abstract
A Bayesian inverse framework is developed to optimize the skill of a predictive numerical model via interpolation of bathymetric measurements to provide the most probable bathymetric surface. The numerical model is a coupled wave flow model and predicts wave and hydrodynamic information (e.g., significant wave height and longshore velocity). The Bayesian method, coupled with Markov chain Monte Carlo (MCMC) optimization, is used to find the bathymetric field, which serves to minimize the residual errors between measured data and the corresponding numerical model results. By using a Bayesian approach, the range of probable model parameters is inferred from the observed data. Monte Carlo simulation is also applied to this numerical model to perform the uncertainty analysis of the model output fields (wave height and flow velocity). This analysis is performed by taking random samples from the probability distribution function (PDF) of inputs and running the model as required until the desired precision (±0.05 m for significant wave height) in output fields is achieved. The case study used in this analysis is the DUCK94 experiment, which was conducted at the US Army Field Research Facility at Duck, North Carolina, in the fall of 1994. The unknown model parameters for the hydrodynamic model involve those controlling bathymetric resolution. Furthermore, the ability of the statistical model to estimate the observed data is tested by running the forward model for two sets of input parameters: the estimated input parameters updated by the previously mentioned statistical model and the prior (noninformative) parameters. Using the model parameters estimated from the Bayesian analysis leads to improved comparisons to data. Using the presented method, the relative errors between the model outputs and the observed data for significant wave height at nearshore gauges is reduced by 30%.
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Acknowledgments
This work was funded by ONR Grants N00014-10-1-0389 and N00014-09-1-0503. We would like to thank Drs. Steve Elgar, Robert Guza, William O’Reilly, and the staff of the US Army Corps Field Research Facility for the data used in this study and Dr. Nathaniel G. Plant for sharing his valuable files.
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Published In
Journal of Waterway, Port, Coastal, and Ocean Engineering
Volume 144 • Issue 6 • November 2018
Copyright
© 2018 American Society of Civil Engineers.
History
Received: Oct 18, 2017
Accepted: May 9, 2018
Published online: Sep 5, 2018
Published in print: Nov 1, 2018
Discussion open until: Feb 5, 2019
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